How to choose an AI consultancy

Choose an AI consultancy by matching the partner to the job, not the brand to the logo slide: a large firm for multi-country governance programmes, an embedded specialist squad when you need working AI products shipped fast, and in-house hiring only once you know exactly what you're building. Here's how to tell which one you actually need.

What are your options?

When teams start looking for AI help, they usually discover the market splits into three very different shapes:

• Large consultancies (the Big-4 and global systems integrators) - deep benches, strong governance and compliance practices, and the scale to run multi-year transformation programmes across many countries.
• Specialist AI consultancies and product studios - smaller, senior teams that embed with yours and are measured on shipped software rather than delivered recommendations.
• Building in-house - hiring your own AI engineers and product people, and owning the capability end to end.

None of these is universally right. The expensive mistake is picking by familiarity - defaulting to the biggest name, or to a hiring plan - before being honest about what the work in front of you actually is.

When does a large consultancy make sense?

If your programme is fundamentally about risk, compliance, and coordination at scale - rolling out AI policy across forty markets, satisfying regulators, integrating with a landscape of legacy vendors - a large firm's machinery is built for exactly that. You're buying assurance and reach as much as engineering.

The trade-off is speed and distance from the work. Strategy and delivery are often separate teams, the senior people who won the deal aren't the people doing the work, and it can take months before anything runs in production. If your goal is a working AI product rather than a governed programme, that model works against you.

When does building in-house make sense?

In-house is the right long-term answer once AI is core to your product and you know what you're building. Your own team compounds knowledge, owns the roadmap, and carries no agency margin.

It's the wrong first move, though, for most teams. Hiring senior AI engineers is slow and competitive, and - as we've argued before - hiring an AI engineer isn't an AI strategy: without a validated use case, new hires spend their first year discovering what a discovery phase would have told you in weeks. A common pattern is to use a specialist partner to find and prove the value, then hire in-house around a system that already works.

When does an embedded specialist squad make sense?

If the goal is a working product - an automation running in production, an agent connected to your real tools and data, software your customers or team actually use - a small senior squad embedded with your team is usually the fastest honest route. The people you meet are the people who build; strategy and delivery are the same team; and progress is measured in shipped software, not documents.

This is the model we run at &above, so treat this section with the scepticism you'd apply to any vendor describing their own category - but the evidence is checkable: an AI creative studio that took Tesco's retail-ad approvals from four weeks to days, an agent system supporting 5,000 Google Cloud sellers, and an AI-powered property app for Upstix shipped in seven days. Scale-ups and enterprises both, delivered by the same small teams. You can see the full model on our AI consultancy & product development services page.

What questions should you ask before signing?

• Who exactly will do the work? Ask to meet the actual team, not the pitch team - and ask what percentage of their time you get.
• What have you shipped? Working software with named clients beats frameworks and accelerators. Ask for something you can use or watch being used.
• What's the smallest first engagement you offer? Good partners derisk with a scoped workshop or proof of concept before proposing a big build; be wary of anyone whose first proposal is a multi-year programme.
• How do you decide what not to build? An honest consultancy should be able to describe AI projects they've talked clients out of.
• What happens when you leave? Ask how knowledge, code, and infrastructure transfer to your team - the answer reveals whether they're building your capability or their dependency.

How do you keep the first engagement low-risk?

Whatever partner you choose, don't start with the big build. Start with the smallest step that produces evidence: a short discovery workshop to find where AI genuinely creates value, then a proof of concept against real use cases before committing serious budget. That sequencing - rather than the day rate - is what actually controls cost, as we've written about in how much AI product development costs.

If you're weighing this decision now and want a specialist's view - including an honest answer on whether you need a consultancy at all - get in touch and we'll tell you what we'd do in your position.